Each year in the United States over 1,000 fatalities occur as a result of collisions with utility poles. In addition, approximately 40% of utility pole crashes result in a non-fatal injury. Moreover, with over 88 million utility poles lining United States highways, it is not feasible to immediately remedy all poles that are potentially unsafe. Utility poles which pose a danger to motorists can, however, be identified and addressed over time in a structured, methodical manner. The goal of this project was to develop a method to identify and prioritize high risk utility poles that are good candidates for remediation as well as develop a standard operating procedure for the relocation of existing utility poles and placement of future utility poles along Massachusetts highways. This research found that the lateral offset, annual average daily traffic and density of the utility poles are major risk factors. Road geometry, however, also impacts the risk. Basic corrective measures such as delineation, placing poles as far from the edge of road as achievable, as well as placing poles a safe distance behind horizontal barriers are all suggested solutions.

The purpose of this study was to investigate predicting student success on one example of a "high stakes" test, the Texas Assessment of Academic Skills Exit Level Exam. Prediction algorithms for the mathematics, reading, and writing portions of the test were formulated using SPSS® statistical software. Student data available on all 440 students were input to logistic regression to build the algorithms. Approximately 80% of the students' results were predicted correctly by each algorithm. The data that were most predictive were the course related to the subject area of the test the student was taking, and the semester exam grade and semester average in the course related to the test. The standards of success or passing were making a 70% or higher on the mathematics, 88% or higher on the reading, and 76% or higher on the writing portion of the exam. The higher passing standards maintained a pass/fail dichotomy and simulate the standard on the new Texas Assessment of Knowledge and Skills Exit Level Exam. The use of the algorithms can assist school staff in identifying individual students, not just groups of students, who could benefit from some type of academic intervention.